Exploring the Morphologies of High-Redshift Galaxies with Deep Learning

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Exploring the Morphologies of High-Redshift Galaxies with Deep Learning

This thesis explores different machine learning techniques for the study of galaxy morphology, morphological classification, and the morphological evolution of galaxies. We utilise data from all of the CANDELS fields imaged with HST, as well as data from the CEERS program imaged with JWST. In Cha...

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Bibliographic Details
Main Author:	Tohill, Clár-Bríd
Format:	Thesis (University of Nottingham only)
Language:	English
Published:	2024
Subjects:	machine learning galaxies redshift morphological classification morphological evolution
Online Access:	https://eprints.nottingham.ac.uk/79446/

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